In order to induce initial translation pairs, we rely on the framework of multilingual probabilistic topic modeling (MuPTM) (Boyd-Graber and Blei, 2009;De Smet and Moens, 2009;Mimno et al., 2009;Zhang et al., 2010), that does not require a bilingual lexicon, it operates with nonparallel data, and is able to produce highly confident translation pairs for high-frequency words (Mimno et al., 2009;Vulić and Moens, 2013).

results reveal that, contrary to conclusions from prior work, the seeding of the bootstrapping process has a heavy impact on the quality of the learned lexicons.


(1) A: Do you think she'd be happy with this book?

when there are direct antonyms for the question word, we only consider those.


Two words of different degree are often regarded as synonyms: even though furious means extremely angry, furious and angry are synonyms in WordNet.

we can extend this idea of extracting an ordering between two words.


Each label is extracted from a longer movie review and reflects the writer's overall intention for this review.

for this nonconvex optimization we use AdaGrad (Duchi et al., 2011) which converges in less than 3 hours to a local optimum.


The expression of these and other kinds of sentiment can be understood as involving three items: (1) An experiencer (2) An attitude (3) A target (optionally) Research in sentiment analysis often focuses on (2), predicting overall sentiment polarity (Agarwal et al., 2011;Bora, 2012).

in (1), although there is a positive sentiment, the target of the sentiment is an event (Kentucky losing to Tennessee).


Nos quedamos accidentados otra vez en la carretera vieja guarenas echando gasoil, estamos a la interperie @[user] good morning, Prof!!

we model the expression of sentiment polarity across the sentiment target itself, extracting both the sentiment target and the sentiment expressed towards it within the same span of words.


We define , ′ , as: The corresponding Gibbs sampler for MDK-LDA will be introduced in § 6.

to incorporate m-sets and deal with multiple senses of a word, the MDK-LDA(b) model was proposed in (Chen et al., 2013a), which adds a new latent variable into LDA.


The numbers of good topics generated by each model are shown in Table 3.

for a comprehensive evaluation, we consider the following variations of MC-LDA and Df-LDA: • MC-LDA: MC-LDA with both m-sets and csets.


If f is not in either translation table, the decoder just copies it directly to the output.

if we extract bigrams based on dependency relations for both languages, the model fits better.


The probabilistic structured query (PSQ) approach was developed using the lexical translation table and the translation table estimated on the decoder's n-best list, both optionally pruned with a variable lower p L and cumulative p C threshold on the word pair probability in the table (Section 3.2).

while processing a pair (q, d), we update W ± h for all h found for the pair.


In our setup, we want P to be a connected undirected graph, and not a directed tree.

10): If t i is co-temporal with t j and t j is co-temporal with t k , then usually t i and t k are either cotemporal or denote the same event.


Temporal event-event relations have been extensively studied (Chambers and Jurafsky, 2008;Yoshikawa et al., 2009;Denis and Muller, 2011;Do et al., 2012;McClosky and Manning, 2012;D'Souza and Ng, 2013), and we leverage such techniques in our work (Section 3.1).

product manufacturing, economical developments, and various phenomena in life and social sciences can all be viewed as types of processes.


Our basic representation is triples of the form (Arg1, Relation, Arg2), extracted from a text corpus using Open Information Extraction (Mausam et al., 2012).

|V | is the number of unique tuples in the corpus.


We see that the effect of applying document normalization in all cases improves the quality of the overall result.

the similarity of the query document to all indexed documents, we compute the value rank i indicating at which position the mate of the i th document occurs.


The significance for results (N 1 ≥ 7000) have been tested by means of a bootstrap resampling significance test, finding out that our results significantly improve on the translation base line at a 99% level.

we quantify the effect of different approximation techniques for computing the orthonormal basis and investigate the effect of various methods for the normalization of frequency vectors.


In fact, both multiple linear regression and support vector regression (Vapnik et al., 1996), which have been widely used in automated essay scoring (Shermis and Burstein, 2002), can be seen as pointwise approaches.

for different prompts, the features used, their weights, and scoring criteria, may be different.


To determine the final rating of each given unrated essay, we have to map this unscaled model score to the predefined scale, such as an integer from 1 to 6 in a 6 point scale.

after each iteration of MART, essays with higher rating tend to receive a higher model score while essays with lower rating tend to get a lower model score.


6 We have proposed a novel Joint, Additive, Sequential (JAS) model of conversational topics and speech acts.

we also plan on extending this model to investigate qualitative questions surrounding patientphysician communication quantitatively.


On the left we show our motivating conceptualization: a latent intent gives rise to both the topic and speech acts; these, in turn, jointly induce a distribution over words and transitions.

to previous work on speech acts, JAS provides a single, coherent generative model of conversations.


There are many ways to map induced schemas to gold answers, and this paper illustrates how extraction performance is significantly affected by the choice.

the schema type variables t only model the distribution of event words (bomb, plant, defuse), but the slot variables s model the syntax (subject-bomb, subject-plant, object-arrest) and entity words (suspect, terrorist, man).


Experiments without them show a slight drop in performance (2 F1 points), small enough that they could be removed for efficiency.

we later evaluate this flat model to show the benefit of added schema structure.


Also, assume that the l th constraint says that the concepts R l and S l should have the same type.

data which came from UPMC (more than 50% data) was not made available for public use.


Recent studies have focused on exploring demographic language variations in personal email communication, blog posts, and public discussions (Boneva et al., 2001;Mohammad and Yang, 2011;Bamman et al., 2012).

corpus-based methods have been used to bootstrap lexicons for ENGLISH (Turney, 2002) and other languages, including ROMANIAN (Banea et al., 2008) and JAPANESE (Kaji and Kitsuregawa, 2007).


In future work, we will try to collect and annotate data for microblogs in other languages to test the robustness of our method.

it is mostly performed on product reviews where opinion targets are always described as product features or aspects.


The basic idea is to use information from the labeled nodes to label the adjacent nodes in the graph.

influence of the parameters total number of candidates extracted by HS+Rule is also less than the other two methods.


In the topic #官 员财产公示# (#Property publicity of government officials#), the first sentence omits the opinion target.

since we mainly focus on Chinese microblogs instead of Twitter in this paper, we will refer to a post as a message.


Related work in quality flaw detection in Wikipedia (Anderka et al., 2012) has relied on meta-features based on edit history, Wikipedia links, structural features and counts of words, sentences and paragraphs.

table 6, we see that all features perform better than the bag-of-words baseline.


However, it groups event tweets into a fixed number of bursty topics and then uses a twostate machine in a postprocessing step to identify events from these bursty topics.

as in many topic models, our topic is a multinomial distribution over words, denoted as ϕ a where a is a topic index.


These findings also have direct implications on authorship attribution of micro-messages, since ksignatures are reliable classification features.

we have shown that authors of very short texts can be successfully identified in an array of au-thorship attribution settings reported for long documents.


We re-trained an existing SRL system on data that is labeled with comparative predicates and arguments.

we do sentence segmentation and tokenization with the Stanford Core NLP 4 .


4 http://nlp.stanford.edu/software/ corenlp.shtml 5 Statistically significant at p < .05 using the approximate randomization test (Noreen, 1989)     piled comparative keyphrases from (Jindal and Liu, 2006a) in addition to the POS tags.

the data is noisy, for the same predicate sometimes entity-is before the predicate, sometimes entity+.


sharper when considering the whole NP rather than its sub-parts; a pressure medicine is still a conceivable concept, but blood (pressure medicine) makes no sense whatsoever.

as the authors also remark, because of their experimental setup, they cannot conclude that the improvement is truly due to the semantic composition component, a crucial issue that is deferred to further investigation.


Alternatively, the candidate set can be defined by all human-suggested substitutions for the given target word in all of its contexts; then, the focus is just on the ranking problem (Erk and Padó, 2010;Thater et al., 2010;Dinu and Lapata, 2010;Thater et al., 2011).

the most important features describe the syntagmatic coherence of the substitution in context, measured as local n-gram frequencies obtained from web data.


(The score of labeled reviews will be fixed to +1 or −1.)

we model each document as a bag of words, generated from a mixture of: (a) "aspect" topics (that discuss various dimensions of the offering); (b) city-specific topics; (c) hotel-specific topics; (d) review-specific topics; 4 and (e) a background topic.


We also thank the EMNLP reviewers for their helpful comments and advice.

approaches that use our RLDA text representation (OUR APPROACH, TWO-LAYER MANIFOLD (R), and REVIEW-SVR+LDA (R)), which tries to remove city-and hotel-specific information, perform better than those that use the full vocabulary (REVIEW-SVR, TWO-LAYER MANIFOLD (S), and THREE-LAYER MANIFOLD (TF-IDF)).


Fixing one of the components of w to any non-zero constant and allowing the others to vary, the new linear model retains the same modeling power, but the (D − 1) free parameters are no longer scale invariant, i.e., scaling the (D − 1)-dimensional vector now has an effect on linear model predictions.

other non-convex problems in machine learning, such as deep neural networks (DNN) and word alignment models, commonly require such initializers in order to obtain decent performance.


By this linear construction, w * is guaranteed to be a global optimum.

this function is optimized by enumerating the up to M hypotheses that form the upper envelope of the model score function.


It is however noteworthy that we can observe some interpretable selectivity in lobe*model combinations.

5 Once both the textual and the visual models are built, we perform two different transformations on the raw co-occurrence counts.


each pronoun (PRONCONJ), we only conjoin with a PRONOUN indicator and add features targeting the person, number, gender, and animacy of the two pronouns (AGR).

rather than explicitly writing a feature targeting definiteness, our indicators on the first word of a mention will capture this and other effects.


In NLP, Moore and Quirk's (2008) random walks from previous local optima were faster than uniform sampling and also increased BLEU scores; Elsner and Schudy (2009) showed that local search can outperform greedy solutions for document clustering and chat disentanglement tasks; and Mei et al.

that parsing model has since been extended to make unsupervised learning more feasible (Klein and Manning, 2004;Headden et al., 2009;Spitkovsky et al., 2012b).


To test how much of this performance could be obtained by a simpler iterated network, we experimented with ablated systems that don't fork or join, i.e., our classic "baby steps" schema (chaining together 15 optimizers), using both DBM and DBM 0 , with and without a transform in-between.

but even the latest techniques can be quite error-prone and sensitive to initialization, because of approximate, local search.


This approach generalizes the state of the art in cross-lingual projection work in the context of POS tagging, and improves upon it.

in our data, over 7000 occurrences of se appear, but only 17 instances have a tag projection that is not filtered by Wiktionary.


But we still found no improvement over using NSF alone.

yelong Shen (Microsoft Research, One Microsoft Way, Redmond, WA 98052, USA, email: yeshen@microsoft.com).


(2014) but with very different applications.

for a given source document, target documents that attract the most clicks are more interesting than documents that attract fewer clicks 1 .


The latter work makes use of translation and joint models, and pre-computes the first hidden layer beforehand, resulting in large speedups.

in comparison to the unidirectional word-based models, phrase-based models perform similarly.


This is confirmed by results presented in (Le et al., 2012), which show that a word-factored translation model outperforms the phrase-factored version.

for our competitive IWSLT 2013 German→English system, we observe gains of up to 1.6% BLEU and 1.7% TER.


Therefore, in this work we continue relying on source and target word vocabularies for building our phrase representations.

for our experiments, we tested 10-best alignments against the single best alignment, which resulted in a small improvement of 0.2 TER on both dev and test.


task, where we include the one-to-one source information, reordered according to the target side.

we experiment with several different ways to resolve ambiguities due to unaligned or multiply aligned words.


The selectional association of a particular noun cluster is then the contribution of that cluster to the verb's preference strength.

for the two-way model, our corpus consists of about 70M tuple instances (1.9M types), so in each fold, about 63M tuple instances are used for training and about 7M (190K types) are used for testing.


This approach takes a collection of images associated with words or tags representing the concept in question.

a flurry of recent results indicates that image descriptors extracted from deep convolutional neural networks (CNNs) are very powerful and consistently outperform highly tuned state-of-the-art systems on a variety of visual recognition tasks (Razavian et al., 2014).


The ESP Game dataset (Von Ahn and Dabbish, 2004) was famously collected as a "game with a purpose", in which two players must independently and rapidly agree on a correct word label for randomly selected images.

images in the ESP dataset do not make a distinction between objects in the foreground and in the background, or between the relative size of the objects (tags for images are provided in a random order, so the top tag is not necessarily the best one).


In a manual assessment, we observed that many of these errors occur if the claim is present in the first paragraph sentence and exhibits preceding indicators like 'first(ly)' or 'second(ly)' which are also frequently used to enumerate premises.

in total, the corpus comprises 90 essays including 1,673 sentences.


Two features represent the number of tokens of the source and target components and the third one represents the absolute difference in the number of tokens.

on the one hand, knowing the type of argument components is a strong indicator for identifying argumentative relations and on the other hand, it is likely that information about the argumentative structure facilitates the identification of argument components.


Since it only contains a low number of attack relations, we focus in this work solely on the identification of argument components and argumentative support relations.

contrary to previous approaches, our approach is capable of identifying argumentative discourse structures even if discourse markers are missing or misleadingly used.


Since many argument components cover an entire sentence (30%), this is not an exclusive feature of this class.

we compared the top 100, 250, 500, 1000, 2500, 5000 word pairs ranked by Information Gain, non-zero Information Gain word pairs and non-filtered word pairs.


scenarios (iii) and (iv) are similar to scenarios (i) and (ii) respectively, but at this time, the users were required to carry out the tests surrounding the movie search domain (which is addressed by a new domain expert not used in the training phase).


If online reinforcement learning is used, we will be unable to evaluate the generated policy after each update, and hence will not know which policy to keep for the final evaluation.

note that, GPTD can also admit recursive (online) computations, but here we focus ourselves on the batch version.


hundreds of thousands of data examples can be easily generated for training and initial policy evaluation purposes, and different reinforcement learning models can be compared without incurring notable extra costs.

recent advances in SDS have shown that statistical approaches to dialogue management can result in marginal improvement in both the naturalness and the task success rate for domainspecific dialogues (Lemon and Pietquin, 2012;Young et al., 2013).


The MDP model and the policy optimisation algorithm are introduced in Section 4 and Section 5, respectively.

we propose an MDP-based approach for learning an optimal central control policy in this section.


Next, a tree subtraction algorithm was used to extract the arguments.

section 2 briefly introduces the PDTB corpus.


It is therefore natural that encoding the output space of argument labeling requires various kinds of information about the connectives, the argument candidates corresponding to a connective, and their argument labels.

the text span that the connective is syntactically attached to is called Arg2, and the other is called Arg1.


Similarly, Georgila (2009) uses Integer Linear Programming post-processing of a CRF to achieve F-scores over 0.8 for reparandum start and repair start detection.

edit terms and reparanda), to approximate a 'fluent' language model.


The much bigger number of incorrect labels from the baseline redecoding badly pollute the encoded knowledge.

following (Jiang et al., 2013), we first train the supervised baseline model, and use it to estimate the potential contribution for each sentence in the Wikipedia training data.


In our pilot study we found that it's beneficial to force a cross-reference or emphasis entity as a word when the item has 2 or 3 characters.

this is 0.72% absolute improvement over supervised baseline (corresponding to 15.19% relative error reduction), and 0.33% absolute improvement over constrained decode (corresponding to 7.59% relative error reduction); the differences are both statistically significant (p < 0.001).


Type one has three characters '年' (year) '月' (month) '日' (date).

for constrained decode it sees 38 cases of "里\B 氏\E" and 190 cases of "里\S 氏\S" in the Wikipedia training data.


There is rich information encoded in online web data.

the middle step, constrained decode, has the risk of reinforcing the errors in the baseline model: the decoded labels added to the training data for building the final model might contain errors introduced from the baseline model.


As we can see, NEs and non-NEs can be better separated using ClusterEmb or DistPrototype features.

semi-supervised learning with generalized word representations is a simple and general way of improving supervised NLP systems.


We carefully compare and analyze these approaches in the task of NER.

fundamental problems on effectively incorporating the word embedding features within the framework of linear models remain.


The output from one task does provide useful information that can improve the other task.

all models are trained using GRMM (Sutton, 2006), with reduced clique orders.


All models are trained using M3N without balancing precision and recall.

*: each conversation produces two long/sentencejoined sequences, one from each speaker.


In this work 1 , we compare various methods for combining punctuation prediction (PU) and disfluency prediction (DF) on the Switchboard corpus.

since the hard cascade method has a greater influence on the features for the second step, it is also more sensitive to the prediction errors from the first step.


In general, longer n-grams might be considered more valuable since they typically lead to better translations and are more relevant for BLEU.

we note that a function m : 2 V → R + is said to be modular if it satisfies the above with equality, meaning If m is modular and m( / 0) = 0, it can be written as m(X) = ∑ x∈X m(x) and, moreover, is seen simply as a n-dimensional vector m ∈ R V .


To learn model parameter W and U, we apply a ranking scheme on candidates selected from phrase table results of each focused phrase.

word position features and partof-speech tagging features are both useful for our bilingual semantic embedding learning.


Table 7 shows the number of standard phrases and hierarchical phrases extracted from the initial and refined word alignments.

we used wMT08 as the development set and tested our system on wMT test sets from 2010 to 2013.


+coarsening takes 0.14 s and +clustering takes 0.16 s while raw label with CCG takes 0.37s.

while their goal is to maximize the posterior we omit the prior term P (M ) and directly maximize the P (D|M ).


We apply the discriminative learning algorithm of Zhang (2013) to train the parameters θ.

the Moses release 1.0 implementations of all three systems are used, with default parameter settings.


Manual comparison does not show significant differences in overall translation adequacy or fluency between the outputs of the four systems.

with these systems, our system relies more heavily on a syntax-based synthesis component, in order to study the usefulness of statistical NLG on SMT.


1: for i = 1 to n do 2: i is a positive instance for C j−i+1 5: end for 9: end for We compare two machine learning methods for learning a series of binary classifiers.

the experimental results showed this feature gave no significant improvement.


Since larger n-gram Language Model (LM) usually performs better in Statistical Machine Translation (SMT), how to construct efficient large LM is an important topic in SMT.

this operation is useful as it can make LM perform better through letting it become larger and larger, by only using a small training corpus.


Section 3 provides details about the resources involved in the experiments.

we concentrate on a specific type of MWEs, namely phrasal verbs (PVs).


Our use of morphological and lexical features overlaps with the AMEANA framework.

the contribution of a partially-matched n-gram is not 1 (as counted in BLEU), but the minimum value that individual tokens within the bigram are credited.


On Zh-En, LR-Hiero+CP applies queue diversity (QD=15) which reduces search errors and improves translation quality but increases the number of hypothesis generation as well.

in this pseudocode, we have introduced the notion of queue diversity (explained below).


This results aligns with the following hypothesis: the word is being perceived and understood by the brain gradually after its presentation, and therefore the brain representation of the word becomes gradually similar to the neural network representation of the word (i.e.

it is also not easy to interpret their very highdimensional and mostly unsupervised representations.


This makes sense considering that they capture much less information than the other two high dimensional descriptive vectors, as they do not represent the complex properties of the words, only a numerical assessment of their likelihood.

brain imaging studies have suggested that during reading, the brain (a) continuously builds a context from the successive words and every time it encounters a word it (b) fetches its properties from memory and (c) integrates it with the previous context with a degree of effort that is inversely proportional to how probable the word is.


(2010); this learning algorithm is incremental and involves limited calculations, thus satisfying basic cognitive plausibility requirements.

(Our corpus contains more than 100,000 utterances.)


Note that the learned similarity scores reflect the semantic distance among words whereas the WordNet scores reflect semantic closeness.

1 st , 2 nd , 3 rd : median ranks of corresponding gold-standard associates given network similarities; size lcc : proportion of network in the largest connected component; σ g : overall "small-worldness", should be greater than 1; %: the percentage of runs whose resulting networks exhibit a small-world structure.


In a small-world network, this value d median is expected to be as small as that of a random graph: even though the random graph has edges more uniformly distributed, the small-world network has many locally-connected components which are connected via hubs.

these models have provided insight about underlying mechanisms of word learning, but none of them consider the semantic relations that hold among words, or how the semantic knowledge is structured.


Another line of work is to model formation of semantic categories but this work does not take into account how word meanings/concepts are acquired (Anderson and Matessa, 1992;Griffiths et al., 2007;Fountain and Lapata, 2011).

the maximum number of edges in the neighborhood of n is k n (k n − 1)/2, where k n is the number of neighbors.


The estimation of P (.|w) is made possible by introducing a set of latent variables, alignments, that correspond to the possible mappings between words and features in a given utterance-scene pair.

when incrementally adding or updating a word's connections in the network, the model only looks at a subset of words rather than comparing the target word to all the nodes in the network.


In the first step of their two-step method, for each uni-modal concept k, a quasi-perceptual representation is computed as an average of the perceptual representations of bi-modal concepts, weighted by the proximity between each of these concepts and k whereC is the set of bi-modal concepts, c p and k p are the perceptual representations for c and k respectively, and c l and k l the linguistic representations.

we therefore investigated whether or not it is preferable to include this additional information as model input or to restrict perceptual input  to concrete nouns as previously.


However, to date, these previous approaches to multi-modal concept learning focus on concrete words such as cat or dog, rather than abstract concepts, such as curiosity or loyalty.

the construction of this array depends on the perceptual information source; the process for our chosen sources is detailed in Section 2.1.


The probability of seeing context word c given target w is defined as: Perceptual P ESP P CSBL Figure 1: Our multi-modal model architecture.

in this case, no method improves on the linguistic-only baseline.


Therefore, we can make better corrections using such context information on nodes.

the CoNLL-2013 shared task is one of the most famous, which focuses on correcting five types of errors that are commonly made by non-native speakers of English, including determiner, preposition, noun number, subject-verb agreement and verb form errors.


For example, Wu and Ng (2013) decodes a global optimized result based on the individual correction confidence of each kind of errors.

instead of using ordinary language models, we should train special language models using the sub-sequences to measure the correctness of a production rule.


The article therefore locates between LP and RP.

manually designed rules usually have exceptions.


This baseline allows direct comparison of frames induced by our SCF model with those derived from a supervised parser's output.

the model is particularly valuable for NLP tasks benefiting from SCFs that are applied across text domains, and for the many tasks that involve sentence-level processing.


LP relaxation algorithms for the MAP problem define an upper bound on the original objective which takes the form of a linear program.

we define the vertex set as V = X ∪ C, with X = {x 1 , .


Another approach is that of Bender et al.

parsing cannot be done directly with standard CKY as with standard PCFGs, so we use the Gibbs sampling algorithm presented in Johnson et al.


Recently proposed dictionary-based text normalization systems perform dictionary look-up and always normalize the same OOV word to the same IV word regardless of the context of the input text (Han et al., 2012;Hassan and Menezes, 2013).

our motivation behind introducing the λ and β parameters was to investigate the importance   of the minor features compared to our major features (described in Sections 3.3 and 3.4).


Since some social media text messages are extremely short and contain several OOV words, they do not provide sufficient context, i.e., IV neighbors, to enable the extraction of good candidates from the word association graph.

to analyze the effects of the lambda and beta parameters, we plotted the performance of the system on the LexNorm1.1 data set by varying their values (see Figure 4).


Each edge in EL(o i ) consists of a neighbor node n j , a candidate node c k and an edge weight edgeWeight(n j , c k ).

the edges that are connected to common words have higher weights.


Recall shows the amount of correct normalizations over the words that require normalization (ill-formed OOV words).

we extended the candidate list obtained through contextual similarity as described in the previous section, by including all the tokens in the word association graph that satisfy the edit distance and 3 an approximate string comparison measure (between 0.0 and 1.0) using the edit distance https://sourceforge.net/projects/febrl/ phonetic edit distance criteria.


This method scales well to large training sets and greatly improves results across a wide variety of corpora, beating current state-of-the-art results by wide margins, including Twitter users (Han et al., 2014, henceforth Han14;Roller et al., 2012, henceforth Roller12); Wikipedia articles (Roller12; Wing and Baldridge, 2011, henceforth WB11); and Flickr images (O'Hare and Murdock, 2013, henceforth OM13).

in addition, because logistic regression does not assume feature independence, complex and overlapping features of various sorts can be employed.


ENWIKI13 especially has dense coverage across the entire world, whereas TWUS only covers the United States and parts of Canada and Mexico.

this can introduce a statistical bias known as the modifiable areal unit problem (Gehlke and Biehl, 1934;Openshaw, 1983).


these scores in MuPtM are induced from their output languagespecific per-topic word distributions.


Since this model computes the similarity with each target word separately for the source word in isolation and its local context, and combines the ob-tained similarity scores after the computations, this model is called Late-Fusion.

in this particular work we present results obtained by a multilingual probabilistic topic model called bilingual LDA (Mimno et al., 2009;Ni et al., 2009;De Smet and Moens, 2009).


During argument identification, there are a large number of identification errors caused by the poor performance of auto syntax trees.

in line 5, the algorithm updates W with the difference (if any) between the feature representations of the best scoring candidate and the gold candidate.


Werdy walks through the flowchart; for each question, we check for the presence or absence of a specific constituent of a clause (e.g., a direct object for Q 1 ) and proceed appropriately until we obtain a set of possible frames.

they rely on a background KB (typically WordNet or extended versions (Navigli and Ponzetto, 2012)), where related senses appear close to each other.


Ideally, each verb sense would be annotated with both the possible clause types or syntactic patterns in which it can occur as well as the possible senses of its objects.

to existing resources, our VOS repository disambiguates both the verb sense and the senses of its arguments.


We will use this proxy to train a classifier to discover the features of good splitpoints which can be generalized and produce a more robust system.

the full features list is explained in table 1.


To optimize Equation 2 it is not practical to search the space of possible S, E combinations (this space is combinatorially large).

temporal Discourse: Events in a soccer match occur in a temporal sequence, and so it is reasonable to assume that the language used to describe them will employ temporal discourse relations to distinguish fragments describing separate events.


If π i is in the set of outgoing edges, this algorithm converges to the original PRA.

our method for knowledge base inference, described in Section 3, performs random walks over a graph to obtain features for a logistic regression classifier.


3 Once we selected the relations, we kept all instances of each relation that had some possible connection in the SVO data.

if one of these relations only appears in the training data and the other only appears in the test data, neither will be useful for prediction.


Each path type π is a feature in our model, and if two path types differ only in one edge type, and the differing edge types have very similar vectors, the resultant feature values will be essentially identical for both path types.

even though they are very large, they are still very incomplete, missing large fractions of possible relationships between common entities (West et al., 2014).


The distribution of a word over these soft clusters assignments was added as features to their classifier.

features based on distributional representations (e.g., raw co-occurrence frequencies) can be computed for every word, given that it occurs in some unlabelled corpus.


Semantic interoperability between resources could represent a viable solution to allow reusability and develop more robust and powerful resources.

for each aligned pair which was considered as good in (Caselli et al., 2014), we have applied a further filtering based on the WN domain system results as follows: if two senses are aligned but do not have the same domain, they are excluded from the WSA results, otherwise they are retained.


The task consists of choosing the most suitable synonym for a word among a set of four options.

we investigate the effects of context filters on thesaurus quality and propose the use of cooccurrence frequency as a simple and inexpensive criterion.


We propose the use of simple cooccurrence frequency as a filter and show that it leads to better results than more expensive measures such as LMI or PMI.

for evaluation, we measure thesaurus agreement with WordNet and performance in answering TOEfL-like questions.


This approach is used in string/string SMT (Brown et al., 1993).

(2006) also presented a symmetric method for training alignment parameters.


(3) computes plausibility scores for dependencies, not penalties.

add node p as the source node of the graph.


(2) combines arc-factored path scores with path-independent scores, and it is desirable to train these two components jointly.

for each modifier token we only consider the dependencies or heads whose probability is above a factor γ of the most likely dependency for the given modifier.


In our multimodel representation, the within class variability is redefined according to both dialogue content (vocabulary) and topic space characteristics (word distributions among the topics).

this figure points out some interesting topics that allow us to distinguish a theme from the others.


In fact, these conversations involve a relatively small set of basic concepts related to transportation issues.

a document can change topics from a word to another one.


In this section, we first give the task definition of two tasks, namely sentiment classification and sentence segmentation.

table 6 shows the macro-F1 of the baseline systems as well as our joint model (JSC) on sentiment classification of tweets (positive vs negative).


Specifically, for example x, we firstly compute its similarity to each of the representative examples, and then the probability of the example x belonging to the positive class and negative class can be calculated using the following formula: In the above formula, A scheme mixing population and individual property is designed to generate the similarity weights of spy examples.

for a spy example x, since we do not know which class it should belong to, enforcing x to the positive class or the negative class will lead to some mislabeled examples, which disturbs the performance of final classifier.


A semantic frame describes conceptual structures, its elements, and their interrelationships.

we thank the anonymous reviewers for their comments.


For instance, in our data, we observed a number of instances of the noun issue with the publication sense (e.g., this week's issue of Sports Illustrated).

the second reason is that the shell content can occur in many different constructions, such as apposition (e.g., parental ownership of children, a concept that allows .


We include as a parameter of the LP model a maximum number of nouns N to admit as LP tags.

finally, which methods of classifying nouns into concepts are most effective at capturing selectional preferences for verb clustering?


We define 7 classification tasks: "Is a verb a member of each category?"

entities: An entity is a mention in the text corre-N: W0-13 e: tree A: walnut Liz had 9 black kittens.


This is because ARIS learns to classify verb categories in sentences and does not require observing similar patterns/templates in the training data.

the third category refers to errors that require knowledge  about set completions.


Table 3 also shows that the sentence categorization is performed with high accuracy even if the problem types and also the verbs are different.

kAZB can deal with more general types of problems such as multiplication, division, and simultaneous equations.


This creates a positive feedback loop -as better weights are learned, the search algorithm is more likely to find confident paths, and more data is available to train from.

to translate this value into a probability we need to incorporate whether the inference path is confidently valid, or confidently invalid.


Therefore "Wěiyuánhuì Shěnyì" is bracketable in this sentence.

if the corresponding target counterpart remains contiguous, we take the source term as a bracketable instance, otherwise an unbracketable instance.


In this section, we investigate to what extent the proposed models affect the translations of test sets.

• Term Translation Disambiguation Model: In this model, we condition the translations of terms in different documents on corresponding per-document topic distributions.


(2011) extend this by further introducing two additional caches.

it does not explore any domain information.


Table 4 shows that while using a translation lexicon learnt from decipherment does not improve the quality of machine translation significantly, the joint approach improves Bleu score by 0.9 and 2.1 on Global Voices test set and web news test set respectively.

since Malagasy and English have very different word orders, we decide to apply dependency based decipherment for the two languages as suggested by Dou and Knight (2013).


In our experiments, we use Turbo parser (Martins et al., 2013) trained on the English Penn Treebank (Marcus et al., 1993) to parse all our English monolingual data.


277-89-002 from the Netherlands Organization for Scientific Research (NWO).

our work overcomes the mutual dependence of sentence and phrase estimates on one another by training both models in tandem.


A representation of T , noted R T is a set of rules in a given formalism that implicitly define T .

we are not aware of other work proposing "lattice rules" as a native format for expressing translational equivalences.


in the speed/memory performances of the decoding.

note here that if there are several RHS for a given non-terminal, we can first merge them by merging their start vertex and end vertex, then apply this optimisation algorithm to obtain a representation with one optimised RHS per non-terminal.


Here, we define semi-formally the terms we will use in this paper.

we are hopeful we can further improve our decoder in the future.


Moreover, even without trained classifiers, this model can be used to estimate potential benefits by plugging in hypothetical error values.

one of the most widely used approaches in CLIR is based on translating each query word s i independently, with probabilities assigned to each translation candidate t ij .


Since modern statistical MT systems generate high-quality translations for many language pairs, this one-best strategy works reasonably well for retrieval and provides a competitive baseline.

one can ask the question "If I had classifiers with x% error on this query set, what would be the benefit of using these classifiers to combine QT methods?"


For query-specific combination, MAP and training data are shown for the most effective experiment of each train-tune setting.

there are two dimensions to consider when comparing labeling strategies.


Table 2), since the selected number of segment clusters (k) was nearly the same (25 for BoW, and 22 for BoS).

to quantify the density of each cor-   pus/representation combination, we counted the nonzero entries of the induced document-synset matrix (alternatively, document-term matrix) and we divided this value by the size of such matrix.


Multilingual knowledge in BabelNet is represented as a labeled directed graph in which nodes are concepts or named entities and edges connect pairs of nodes through a semantic relation.

the order in which this process is done can seriously impact the results.


around 26% of the cases we observe a decreased MAP score, which is on average 0.16 smaller then the scores achieved with the baseline.

figure 2 shows the case, where the ranking is performed correctly, but the precise substitute is not annotated as a correct one.


This non-negligible human intervention might not be generic enough to conveniently scale up to new databases with other schema, broader vocabularies or languages other than English.

let N W denote the total number of words and N S the total number of entities and relation types.


We aimed for a 50/50 ratio because both methods perform similarly.

the scale and the difficulty for machines to interpret natural language still makes this task a challenging problem.


table 2 shows the token-level results on our indomain test set.


Our model improves upon a contrastive max-margin objective function from previous work to dynamically update answer vectors during training with a single model.

consider factoid question answering: given a description of an entity, identify the person, place, or thing discussed.


Fortunately, hundreds of questions are produced during the school year for quiz bowl competitions, yielding many different examples of questions asking about any entity of note (see Section 4.1 for more details).

a minor character in this play can be summoned by a bell that does not always work; that character also doesn't have eyelids.


Given a set of mentions M from the previous step, The next step is to select a concept t ∈ C(m) for each m ∈ M. We take the common approach of ranking the candidate concepts.

because Milne and Witten were focused on general text, they computed SR only between t and the unambiguous mentions (i.e.


Generating gold standard summaries is expensive and time-consuming, a problem that persists with cross-language query biased summarization because those summaries must be query biased as well as in a different language from the source documents.

we have approached cross-language query biased summarization as a stand-alone problem, treating the CLIR system and document retrieval as a black box.


As common in previous work (Yan et al., 2013;He et al., 2012) we assume that our generator operates in an interactive context.

besides adhering to the above formal criteria, poems must exhibit concise and accurate use of language, engage the reader/hearer, stimulate their imagination, and bring out their feelings.


Given that we have a framework for generating general summaries, and the current popularity of the query-focused summarization task, we propose that the bulk of future work in this area be focused on the query-focused task specifically in assessing the relevance of a summary to a provided query.

the third algorithm in our experiment uses Approximate Policy Iteration (Lagoudakis and Parr, 2003) to implement a reinforcement learner.


The algorithm performs N summarizations from a blank state to termination, updating a set of state-value predictions as it does so.

we can see from the absolute differences in score that very few additional important words were extracted (ROUGE-1) however REAPER showed a significant improvement in the structuring and ordering of those words (ROUGE-2, and ROUGE-L).


Unlike the word-based operation, some of these models e.g (Knight and Marcu, 2000;Siddharthan et al., 2004;Turner and Charniak, 2005;Galanis and Androutsopoulos, 2010;Wang et al., 2013), are tree-based approaches that operate on the parse trees and thus the compression decision can be made for a constituent, instead of a single word.

if the hypothesis is incorrect, we update the feature weights using the structured perceptron learning strategy (Collins, 2002).


• If a node's label is 'SBAR', its parent's label is 'NP' and its first child's label is 'WHNP' or 'WHPP' or 'IN', then if we can find a noun in the left siblings of 'SBAR', this subordinate clause could be an attributive clause or appositive clause.

automatic summarization can be broadly divided into two categories: extractive and abstractive summarization.


These compressive summaries often contain more information than sentence-based extractive summaries since they can remove insignificant sentence constituents and make space for more salient information that is otherwise dropped due to the summary length constraint.

we can extract rich features for every node in the constituent parser tree.


In this paper, we reported the first steps for a multidocument summarization system for Turkish.

deriv: Using the Root method may oversimplify words because some words that are derived from the same root may have irrelevant meanings.


It is now possible to reach extensive and continuously growing amount of resources.

if a morphological analysis method is applied to the sentences before giving them to the summarization system, words with similar meanings can match during the sentence similarity calculations.


His past research focused on support vector machines, text classification, structured output prediction, convex optimization, learning to rank, learning with preferences, and learning from implicit feedback.

thorsten Joachims is a Professor in the Department of Computer Science and the Department of Information Science at Cornell University.


For GS11 and KS14 the utterance tokens are POS-tagged and lemmatized; for NWE, we test the vectors in both a lemmatized and an un-lemmatized version of the corpus.

in a further step, the vector space was normalized and projected onto a 300-dimensional space using singular value decomposition (SVD).


Additionally, neural vectors have also proven successful in other tasks (Mikolov et al., 2013c), since they seem to encode not only attributional similarity (the degree to which similar words are close to each other), but also relational similarity (Turney, 2006).

co-occurrence-based word representations One way to produce such vectorial representations is to directly exploit Harris (1954)'s intuition that semantically similar words tend to appear in similar contexts.


Motivated by the recent success of deep architectures in general and deep recurrent networks in particular, we explore an application of deep bidirectional RNNshenceforth deep RNNs -to the task of opinion expression extraction.

another way to incorporate information about the future is to add bidirectionality to the architecture, referred as the bidirectional RNN (Schuster and Paliwal, 1997): where  In this setting − → h t and ← − h t can be interpreted as a summary of the past, and the future, respectively, around the time step t. When we make a decision on an input vector, we employ the two intermediate representations − → h t and ← − h t of the past and the future.


We use the standard multiclass cross-entropy as the objective function when training the neural networks.

their structure fits many NLP tasks, when the interpretation of a single sentence is viewed as analyzing a sequence of tokens.


We use a neural network which consists of a weight matrix W 1 ∈ R n×n for left children and a weight matrix W 2 ∈ R n×n for right children to compute the vector for a parent node in a bottom up manner.

consider an example of head h and its dependents x, y (we ignore directions for simplicity) in Figure 5.


Experimental results also verify the success of the cube activation function empirically (see more comparisons in Section 4).

for Chinese, we adopt the same split of CTB5 as described in (Zhang and Clark, 2008).


In the arcstandard system, a configuration c = (s, b, A) consists of a stack s, a buffer b, and a set of dependency arcs A.

6 over 99% of the trees are projective in all datasets.


In the binary classification task for predicting reason i, we train a binary classifier c i using the one-versus-all training scheme.

as before, we employ MEMM to learn how to predict stance sequences.


Our system misclassifies the second example for ABO in part because the first part of the sentence (i.e., Sure, the fetus has the potential to one day be a person) expresses a meaning that is completely inverted by the second part.

if a test post is predicted to have a positive (negative) stance, then only the reason classifiers associated with the positive (negative) stance will be used to predict the reasons it contains.


, R n ) that jointly maximize P (R, S|P S ).

none of the models we have presented so far makes use of the reasons predicted for the preceding post.


Let p be an overt pronoun in P R, the set of the 10 overt pronouns described in Section 3.

after training, we can apply the resulting model to resolve aZPs.


Early approaches to Chinese ZP resolution are rule-based.

for example, to resolve ZPs in Spanish texts, ferrández and Peral (2000) proposed a set of handcrafted rules that encode preferences for candidate antecedents.


The model incorrectly resolves the AZP *pro* to 行政区 (Its administrative area).

person has three values, first, second and third.


To investigate the contribution of context and each attribute to overall performance, we conduct ablation experiments.

recall that we employed eight context features to encode the relationship between a pronoun and a candidate antecedent.


Below we describe the details of the E-step and the M-step.

the two models only agree on the antecedents of 55% of the AZPs in Setting 1.


But, a person looking at a picture of one would probably call it a bird (unless they are very familiar with ornithology).

human Computation Games: Games can be a useful tool for collecting large amounts of labeled data quickly.


For simplicity, we assume each tuple in U A has score 1.0 and each tuple in U B has score 0.2.

every technique studied in this paper has its own merit in template generation.


All of our filters are applied to this base setup.

we apply a series of increasingly restrictive filters over this set.


The Genia Event Extraction task is arguably the most important of all the tasks proposed in BioNLP-ST and is also the only task organized in all three events in the series.

these steps ensure that the weights generated from different initialization points have smaller variance.


If the candidate trigger is not a trigger, the class label of the corresponding instance is N one; otherwise, the label is the type of the trigger.

here, n i denotes the number of trigger candidates in sentence i.


This is because most word tokens are one or two syllables, and only one of the two rhyme types of the weight grammar included a coda.


The earliest Simple Recurrent Network (SRN) modeling experiments of Christiansen et al.

it is possible that syllable weight was more useful because modeling syllable weight involves modeling the characteristics of codas; the advantage may not have been due to weight per se but due to having learned something about the effects of suffixes on final codas.


Turning to the 32, 536 word tokens with no stress (i.e., the function words), all but 23 were monosyllabic (the 23 were primarily contractions, such as "couldn't").

that work uses stress-marked input prepared by marking vowels that are listed as stressed in a pronouncing dictionary.


Typical statistical models includes Hierarchical Dirichlet process (HDP) model (Goldwater et al., 2009), Nested Pitman-Yor process (NPY) model (Mochihashi et al., 2009) etc, which are actually nonparametric language models and therefor can be categorized as word-based model.

table 2 summarizes the F-Scores obtained by different models on PKU and MSRA corpus, as well as several state-of-the-art systems.


Another disadvantage is the requirement for a manually segmented training corpus to find best value for parameters (they called it proper exponent).

compared to nVBE, the joint model correctly solves 171/871 more combinational ambiguities on PKU/MSRA corpora, which is a 0.6%/13.8% relative error reduction.


However, most previous work train a CRF by using full annotation only.

in this scenario, we assume that there are unlabeled sentences along with a lexicon for the target domain.


For instance, it increases segmentation accuracy by 4% when added to the classifier (CNP and CP in table 4).

syllabic units, however, rival the performance of morphological units when used in KWs.


Table 3 (column "With Mapping") additionally shows the accuracy using simple tagset mapping, i.e.

the contribution of the regulariser is scaled by the constant 1 2σ 2 .


Firstly, we want to know how noisy the projected data is.

yarowsky and Ngai (2001) pioneered the use of parallel data for projecting tag information from a resource-rich language to a resource-poor language.


The idea of using the universal tagset is of great use in multilingual applications, enabling comparison across languages.

showed that a 1,000 token dataset could be collected very cheaply, requiring less than 2 hours of non-expert time.


The supervised model trains a maximum entropy tagger using the same features as in Table 4 on this 1000 tokens.

we automatically correct the mapping, and other incompatibilities arising from incorrect alignments and syntactic divergence between the source and target languages.


The DPM result of 80.2% accuracy is encouraging, particularly because the model had no explicit supervision.

our aim is to see how effectively we can exploit the strengths of the DPM model while correcting for its inadequacies using direct supervision.


We chose an initial training set T 1 of ∼5 sequences on CoNLL2000 and CoNLL2000POS datasets, ∼7 sequences on CoNLL2003 dataset and ∼4 sequences on NLPBA2004 dataset, for a total of ∼100 labeled tokens for each dataset.

after the choice of the most informative token the sets L i , U i and T i are updated.


In Table 1 we report the unlabelled (UAS) and labelled (LAS) attachment scores for the static and the dynamic oracles.

first, the range of variable i in the loop in line 8 starts at max{0, − d}, rather than at 0, because we do not need to combine pairs of items originating from nodes in σ below the topmost node, as the items resulting from such combinations correspond to computations that do not contain our input configuration c. Second, when we have set values for i such that i+2 < , we can omit calling PROCESS-CELL for values of the parameter k ranging from i+2 to −1, as those calls would use as their input one of the items described above, which are not of interest.


A transition-based dependency parser is a nondeterministic device, meaning that a given configuration can be mapped into several configurations by the available transitions.

naïve implementations of dynamic oracles run in exponential time, since they need to simulate all possible computations of the parser for the input configuration.


The algorithm uses a two-dimensional array T whose indexes range from 0 to |γ| = + |β R |, and only the cells T [i, j] with i < j are filled.

the oracle needs to make cell updates in a more general way, which includes linking pairs of elements in the reduced buffer or pairs of inferred entries in the table.


A conjunctive head-head relation between a verb and a noun is rare.

this is because sense ambiguities are partially resolved by PoS tagging, and the general problem of sense disambiguation is beyond the scope of this paper.


Section 5 contains our concluding remarks.

the unsupervised approach accumulates errors caused by automatic annotation processes, such as word segmentation, PoS tagging, syntactic parsing, and semantic role assignment.


(2011) seeks to solve NLP tasks without depending on the feature engineering of conventional NLP methods, our architecture is more practical because it integrates the neural networks into a well-tuned conventional method.

our tagger outperform Bohnet and Nivre (2012), so we believe this is the highest PoS accuracy ever reported for a tagger trained on this data set.


P1+S1 Humans have their own rights and privacy .

precision, as mentioned in the overview paper (Ng et al., 2014), is much harder to compute because systems typically do not categorize their corrections by error type.


(2010;Plank and Moschitti (2013) and Søgaard and Haulrich (2011) have previously tried to use importance weighting to correct sample bias in NLP.


The best EASY-CCG model is roughly four times faster than the C&C parser 2 .

these dependencies capture a deeper information-for example by assigning both boy and girl as subjects of talk in a boy and a girl talked.


If we assume all derivations licensed by our grammar are equally likely, and that lexical category assignments are conditionally independent given the sentence, we can compute the optimal parseŷ as: As discussed in Section 2.1, many derivations are possible given a sequence of lexical categories, some of which may be semantically distinct.

performance was much worse, with an F-score of 79.63% on the 97.8% of sentences it parsed on CCGBank Section 23.


Word representations are hard to train due to the computational complexity.

this is usually problematic due to the homonymy and polysemy of many words.


use the grammar of pregroups as the syntactic machinery to construct distributional meaning representations, since both pregroups and vector spaces can be seen as examples of the same abstract structure, which leads to a particularly clean mathematical description of the compositional process.

sKMat A third option for generating a sentence vector with s = 2 dimensions is to consider the verb as an s × K matrix.


town IDEALIZE authority or child IDEALIZE racehorse).

tensor produces almost equal numbers of false positives and false negatives, but sometimes produces false negatives with low frequency nouns (e.g.


We do not split queries into clusters or subtopics relevant to the original query to indicate a intent, but link them in an graph with intent feature similarity, weakly or strongly, in a holistical view.

related work and conclusions are presented at the end of the paper.


Run R Q fails to extract any facts for two of the target classes, SkyBody and SportEvent.

furthermore, both queries and sentences may not be useful questions from which relevant facts can be extracted, even when they match the extraction patterns.


In general, the phenomenon affects facts extracted from text by previous methods (Van Durme and Paşca, 2008;.

in (Tokunaga et al., 2005), the patterns "A of D" or "what is the A of D" extract noun-phrase A attributes from queries and documents, for phrase descriptors D of the class.


The model will learn the weights of each clause to determine the most effective patterns for semantic triple construction.

then, we present the predicates and the first-order logic formulas used in the model.


The results demonstrate that only 51.5 and 23.8% of the NEs are correctly recognized, respectively.

the growing amount of Linked Data contains a wealth of knowledge, including entities, classes and relations.


Previous methods (Lopez et al., 2006;Freitas and Curry, 2014) have usually performed disambiguation at each step only, and the subsequent step was performed based on the disambiguation results in the previous step(s).

we subtract one weighted formula from the original sets at a time, except retaining the first two formulas sf1 and sf2 for basic inference.


We believe this is because the three tasks (phrase detection, phrase mapping, and semantic item grouping) are connected with each other.

dEANNA extracts predicate-argument structures from the questions using three hand-written patterns.


y k ∈ {1, −1} is the outcome: if competitor i beats competitor j, y k = 1; otherwise, y k = −1.


Previous studies propose to solve this problem based on the question-user comparisons extracted from the question answering threads.

we take advantage of the observations, and assume that if two questions are close in their textual descriptions, they will also be close in their difficulty levels, i.e., the smoothness assumption.


The answerer who provides the best answer is called the best answerer u b .

for newly posted questions without any answers received, no competitions could be extracted and none of the above methods work.


Of the three possible answers for this question, "Good move", "Bad move", and "Undecided", our baseline system without demographics believes that user 2252750 probably voted for the third, as "nobody gives a rip" makes him sound apathetic toward the issue.

performance drops considerably and significantly at all four training set sizes after removing Voterpair constraints, suggesting that in this setting, Voterpair constraints are still the most important of our proposed improvements.


Based on other work (Fox Tree and Schrock, 1999;Fox Tree and Schrock, 2002;Groen et al., 2010;Walker et al., 2012b), we also present our classifiers with features representing the first lemmatized unigram, bigram, and trigram appearing in each comment.

it would also be reasonable for our system to exploit the QuestionPair and VoterPair constraints described in Section 5.


The node set comprises of stocks, , ∈ stands for the edge between stock nodes and and the edge weight is the number of co-occurrences.

this paper proposed to build a stock network from co-occurrences of ticker symbols in tweets.


Figure 1 demonstrates a segment of the stock network constructed from our dataset.

ssN is built on large statistics of human recognition in social media, which is likely to be more reliable as shown.


This resulted in a dataset of N = 75,394 users, who wrote over 300 million words collectively.

this paper describes the creation of age and gender predictive lexica from a dataset of Facebook users who agreed to share their status updates and reported their age and gender.


Over blogs and Twitter, both age and gender prediction accuracies drop to a greater degree (when only using the Facebook-trained models), suggesting stylistic or content differences between the domains.

although some words no doubt have a nonlinear relationship with age (e.g., 'fiance' peaks in the 20s), we still find high accuracy from a linear model (see Table 1) and it allows for a distribution of the model in the accessible form of a lexicon.


Demographic lexica have potential for widespread use in social science, economic, and business applications.

when using lexica created with data from across multiple domains, the results in Facebook, blogs, and Twitter remain in line with results from models created specifically over their respective domains.


ProPPR allows a fast approximate proof procedure, in which only a small subset of the full proof graph is generated.

we introduce a probabilistic programming dependency arc prediction approach, where theory engineering is made easy.


So as not to limit the inferences that may be drawn, our annotations include events that are +effect or -effect either the agent or object.

the other set is used as a seed set by the graph model, and as a training set by the gloss classifer.


Compared to Table 4, while +effect and Null classes show a slight improvement, the performance is degraded for -effect.

we build a graph-based model in which each node is a WordNet sense, and edges represent semantic WordNet relations between senses.


Although they make use of both WordNet glosses and relations, and gloss information is utilized for a classifier, this classifier is generated only for weighting edges between sense nodes and classification nodes, not for classifying all senses.

it has a positive effect on the object, a crime, since performing a crime brings it into existence.


There may be better methods to use seed words for aspect discovery (Jagarlamudi et al., 2012;Mukherjee and Liu, 2012), and it would be interesting to combine their methods with ours.

by incorporating the sentiment lexicon, for RMSE, SATM achieves 10% improvement over SATM-O and 7% improvement than Overall.


To tackle this problem, models in the literature usually use some seed words for each sentiment topic to define Dirichlet priors with asymmetric concentration parameter vectors (Sauper et al., 2011;Kim et al., 2013), or use seed words to initialize word assignment to sentiment topic (Lin and He, 2009), or both Jo and Oh, 2011).

ω 0 k and ω b are the aspect-specific and corpus-level bias, respectively.


Global Prediction (Lu et al., 2009) trains a multi-class classifier to classify the sentiment word m into a rating category r ∈ 1, 2 .

this is beyond the scope of this paper, and we list it as future work.


We use a Prefix Tree (Trie) data structure to represent all possible prefixes of the expanded hashtag phrases, but the prefixes consist of words instead of characters.

first, we created SVM classifiers for each emotion using N-gram features and performed 10-fold cross-validation on the test data.


For example, #angryattheworld and #angryatlife both have the prefix "an-gry at", which suggests the emotion ANGER.

researchers have also started to utilize the hashtags of tweets, but primarily to collect labeled data (e.g., for sarcasm (Davi-   and for sentiment/emotion data (Wang et al., 2012;Mohammad et al., 2013;Choudhury et al., 2012;Purver and Battersby, 2012;Mohammad, 2012a)).


Our method takes full advantage of the link information that is languageindependent.

in the neighbor pages of a certain page, the popular pages are the minority.


Here, the horizontal axis represents the discount factor and the vertical axis represents the F-score.

it is very hard to tune the because it varies a lot in different websites and language pairs.


In this paper, we conduct the experiments on English-Chinese parallel page pair mining.

a set of English pages and a set of Chinese pages are obtained.


Identifying parallel web pages from bilingual web sites is a crucial step of bilingual resource construction for crosslingual information processing.

a lot of previous research has focused on automatically mining parallel corpora from the web.


Technical contributions that make re-tuning possible are a crossentropy objective, prefix decoding, and dynamic phrase table augmentation.

subjects could move among sentences within a screen, but could not revise previous screens.


assume we have a preference of a higher G (e.g., BLEU or 1−HTER).


Typing the prefix of this string will update the autocomplete dropdown with matching strings from the gray text.

when experimental factors are sampled from larger populations-e.g., humans, sentences, words-LMEMs are more robust to type II errors (see: Baayen et al.


Larger quantities of corrections should yield further gains, but our current experiments already establish the feasibility of Bar-Hillel's virtuous "machine-post-editor partnership" which benefits both humans and machines.

he used undergraduate, non-professional subjects, and did not consider re-tuning.


To do that, we topologically sort the nodes in V at time t = 0 and maintain a sorted list of nodes as we refine g throughout the search -thus avoiding having to recompute the partial ordering of the nodes at every iteration.

with k < 10 4 both model scores and translation quality can be improved.


For example, the phrase pair pp 9 has a swap orientation (o 9 = S) with respect to a hierarchical block (h 1 ) that comprises the five preceding phrase pairs.

their approach is limited to small training sets of a few thousand sentences and a similar number of sparse features.


The work of the first author is supported by a CIFRE grant from French ANRT.


The standard configuration of modern phrasebased Statistical Machine Translation (SMT) (Koehn et al., 2003) systems can produce very acceptable results on some tasks.

first, concerning the rewriting phrase table, for the k=5 (rpt5pef) and k=10 (rpt10pef) configurations 9 a decrease of 0.7-0.8 BLEU over reranker is obtained.


Since we only have access to executions and treat parse trees as latent, we consider as correct all parses that produce correct executions.

the majority of approaches treat grammar induction and parameter estimation separately, e.g.


We describe the choices of hyperparameters and details of our feature set in §5.

since exact inference is prohibitively expensive, we follow previous work and perform bottom-up beam search, maintaining only the k-best categories for each span in the chart.


In the studied navigation domain, the word chair is often used to refer to chairs and sofas, as captured by the first two entries.

our learning algorithm design combines aspects of previously studied approaches into a batch method, including gradient updates (Kwiatkowski et al., 2010) and using weak supervision (Artzi and Zettlemoyer, 2011).


The templates in Table 5 handle the major cases of missing words seen in our data and are more efficient than the approach taken by (Zettlemoyer and Collins, 2007) who introduced complex type shifting rules and relaxed the grammar to allow every word order.

our work is unique in studying the use of related ideas for semantic parsing.


The respective system model (SM) is a code-base hierarchy containing the classes USER, BUTTON, DISPLAY, the method BUTTON.CLICK() and the property DISPLAY.COLOR.

the requirements we aim to parse are given in a simplified form of English (specifically, the English fragment described in ).


An event e can also refer to a state, where a logical expression is being evaluated over a set of property:value pairs.

table 4 presents the results of the discourse-based model for N > 1 on the Phone example.


Typically, different existing models employ different assumptions for establishing such connections, leading to very different definitions of joint representations.

such a joint representation conveys more information, but requires languagespecific syntactic analysis.


The model can be used to handle both tree structured semantic representations and lambda calculus expressions, and assumes there exist CCG derivations as joint representations in which each semantic unit is associated with a contiguous word sequence where overlappings amongst word sequences are not allowed.

in practice, we define features at each level of the relaxed hybrid tree structure h. features are defined over (n, m) tuples where n is a contiguous sequence of natural language words (immediately or remotely) associated with the semantic unit m (recall that h contains both n and m, and each level of h simply consists of a semantic unit and a contiguous sequence of words).


For the Yelp dataset, the number of anchor words varies from 59 to 69, and 43 out of those are shared across at least four trials.

adding more documents would not affect the size of the co-occurrence matrix, requiring more times to construct the cooccurrence matrix at the beginning.


The anchor words algorithm performs provably efficient topic model inference by finding an approximate convex hull in a high-dimensional word co-occurrence space.

this order is listed as # in the previous table.


Simultaneous translation has been dominated by rule and parse-based approaches (Mima et al., 1998a;Ryu et al., 2006).

both elements of the system require substantial refinement before they are usable in a real-world system.


Moreover, the results obtained are very close to the best results one can ever hope to obtain from any CNF grammar on WSJ10 (CNF-UBOUND) (Klein, 2004).

although in these cases we still managed to identify the target language or have a good relative entropy result, the bracketing results were in general not good.


We described a learning algorithm for this class which scales well to large examples and is even capable of learning from small samples.

we ran the second phase of our system in a supervised fashion by using the treebank to decide which are the best congruence classes to choose as non-terminals.


All uppercase letters were converted to the corresponding lowercase ones.

for example, an essay written in English produced by a french native speaker that is also proficient in German, could be identified as either french or German.


(2012) was also used in this experiment.

a modified version of the corpus that has been normalized as much as possible for topic and character encoding (Tetreault et al., 2012) is used.


we define "merging" two weighted graphs as creating a weighted graph whose adjacency matrix is the sum of the two adjacency matrices of the two weighted graphs.


µ > 0 is the only hyper-parameter of LLGC: it determines how strong the penalization based on the cluster assumption should be.

educational experts have been continuously studying methods for measuring the size of a learner's vocabulary, i.e., the number of words the learner knows, over the decades (Meara and Buxton, 1987;Laufer and Nation, 1999).


Another alternative method is Information Gain (Yang and Pedersen, 1997).

previous research in this area has linked the absence of subjunctive linguistic structures in Chinese to different cognitive representations of the world and consequences for thinking counterfactually (Bloom, 2014), although this has been disputed (Au, 1983;Garbern Liu, 1985).


It has been posited that this is likely due to shared culture and teaching environments (Malmasi et al., 2013).

as defined in equation 2, it measures the entropy gain associated with feature t in assigning the class label c. these methods are limited: they do not provide ranked lists per-L1 class, and more importantly, they do not explicitly capture underuse.


This paper explores the relationship between reading devices and readability.

then, the news article recommendation using the device-dependent readability is explained.


The point of this figure is to measure how appropriate a news article is for a specific reading device.

according to their results, the average number of verb phrases in a sentence, the number of words in an article, the likelihood of the vocabulary, and the likelihood of the discourse relations are highly correlated with human ratings.


Although both users read the same article, user A might believe that her article is more difficult to read than user B because of the screen size of her smart phone.

the top three factors are superficial with regard to text length.


For the background corpus B, we collected news articles from January 1, 2013 to September 6, 2013, but excluded the articles from June 10 to June 25, because they are already used.

the average number of words in a sentence is 15.73, whereas a sentence length ranges from 4 to 100 words.


The Galaxy Note 1 with a 5-inch screen is used as the smart phone, Galaxy Tab 10.1 with a 10.1inch screen is used as the tablet, and A4-size paper is used for the paper.

françois and faircon (2012) proposed a readability formula with 46 textual factors for french as a foreign language.


Such neologisms make it difficult to read and understand a text.

the order between ranks a 1 a 2  ... a m should be satisfied by the criterion of the recommendation system.


According to their results, the average number of verb phrases in a sentence, the number of words in an article, the likelihood of the vocabulary, and the likelihood of the discourse relations are highly correlated with human ratings.

the second important factor is WS, whose correlation is 0.278.


Note that WS and LL are important readability factors for all devices.

pitler and Nenkova (2008) showed the relation between readability factors and readability.


Recent studies on readability use various factors including syntactic ones, and combine them to produce a highly predictive model of readability.

with the increasing use of hand-held devices, people in these days use various reading devices such as a tablet and a smart phone as well as a paper.


In order to reduce the size of the search space, we further propose pruning constraints that are learnt from the training corpus.

reasonable decoding by considering all possible labeling of sequences may improve the performance.


Note that D relies crucially on document segmentation in the reference corpus, which is not encoded in the cooccurrence networks derived by the method described above.

results from an LDA model fit to our corpora, as well as from a sample of published topics, are provided as a baseline to calibrate readers' intuitions about coherence 2 .


A general form of semantic coherence can be defined as the mean pair-wise similarity over the top n words in a topic or cluster t where S is a symmetric measure of similarity.

this approach discards some factors for which one might want to account, such as centrality in the network of communities and their composition.


Recently, Levy and Goldberg (2014) have generalized the skip-gram model to include not only linear but arbitrary contexts like contexts derived from dependency parse trees.

positive training examples are collected by checking if the 500 nearest text-based neighbors of w contain one of the antonyms listed by Webster.


7 From the two tables, we observe that the expansion either with or without syntactic information outperforms the baselines.

f (d i ) > f (d j ) means that the model asserts that c i c j , where c i c j means that c i is ranked higher than c j ; that is, the company c i is more risky than c j .


Although the writing style analysis is an old research area and has been applied successfully to solve many problems, notably authorship attribution, it is obvious that its application to identify the plagiarized fragments still needs to be investigated further.

the n-grams with the maximum number of occurrences) will be in the class m−1, and the least frequent n-grams (e.g.


Let S denotes the set of these fragments.

if many of the character n-grams of a fragment are infrequent in the document, it would be probably a plagiarized fragment.


Intuitively, this task could be framed as an ad-hoc IR problem, where the spoken document is treated as an information need and each sentence of the document is regarded as a candidate information unit to be retrieved according to its relevance to the information need.

the enhanced query model is termed query-specific mixture model (QMM), and its corresponding training objective function can be expressed as Language modeling is a critical and integral component in any large vocabulary continuous speech recognition (LVCSR) system (Huang et al., 2001;Jurafsky and Martin, 2008;Furui et al., 2012).


Researchers in the NLP community have studied power and influence in various genres of interactions, such as organizational email threads (Bramsen et al., 2011;Gilbert, 2012;Prabhakaran and Rambow, 2013;, online discussion forums (Danescu-Niculescu-Mizil et al., 2012;Biran et al., 2012) and online chat dialogs (Strzalkowski et al., 2012).

the variation in the topic shifting tendencies is significantly correlated with the candidates' recent poll standings.


This work was supported by NSF IIS1218282, NSF IIS1218749, NSF IIS1111142, NIH R01GM093156, the U. S. Army Research Laboratory and the U. S. Army Research Office under contract/grant number W911NF-10-1-0533, the NSF Graduate Research Fellowship Program under Grant No.

to the few previous low rank language modeling approaches, PLRE is not orthogonal to n-gram models, but rather a general framework where existing n-gram smoothing methods such as Kneser-Ney smoothing are special cases.


Leveraging this property, it is straightforward to prove the following lemma: Lemma 1.

while this statement is not true under the 2 norm, it is true under generalized KL divergence (Lee and Seung, 2001): generalized KL divergence preserves row and column sums: if M (κ) is the best rank κ approximation of M under gKL then the row sums and column sums of M (κ) and M are equal (Ho and Van Dooren, 2008).


The query can prove either the correctness or incorrectness of the answer being considered.

avg Min Max # of triggers 7.0 2 18 # of arguments 11.3 1 36 # of relation 7.9 1 37 Table 1: Statistics of triggers, arguments and relations over the 200 annotated paragraphs.


A compelling use case of a sensorial lexicon is that automatic text modification to change the density of a specific sense could help people with sensory disabilities.

table 2 demonstrates the observed agreement at both word and sentence level.


Another observed shortcoming of LSA is its failure to correlate the names of the colors with sight while this association is explicit for the annotators.

the second question was devoted do determining word-sense associations.


One straight-forward idea is that the dot product of perspective vectors P k • P l should be a measurement of correlation between perspectives.

after choosing the hyper-priors, the only remaining parameter to tune is the dimension of the latent vectors.


We evaluated the word embeddings with GRE antonym questions, and the result achieves the state-of-the-art performance.

we focus on fusing the information from thesauruses and automatically induced word relatedness measures during the word vector space creation.


Emphasizing head words is helpful in representing composed meanings, but in the case of verbs it may not always be sufficient.

this experimental result suggests that word2vec can serve as a strong baseline for these kinds of tasks, in addition to word analogy tasks.


If a model is fundamentally misspecified, then no amount of additional training data can lower its bias.

a latent variable z i is created for every mention i, where z i ∈ R ∪ {None} takes a single relation label, or a no relation marker.


For instance, part-ofspeech tags can be viewed as the "types" of words.

although the type information and the constraints are readily available, it is overlooked in the previous work on matrix and tensor decomposition models for knowledge bases (Riedel et al., 2013;Nickel et al., 2012).


Results presented in Table 2 (right) show that TRESCAL achieves 76.12%, while RESCAL and TransE are 73.15% and 75.88%, respectively.


Matrix and tensor decomposition methods have been applied to modeling multi-relational data.

initializing the model properly might be important for the performance.


In addition to knowledge embedding, another interesting approach is the word embedding method word2vec (Mikolov et al., 2013b), which shows that learning word embeddings from an unlabeled text corpus can make the vectors connecting the pairs of words of some certain relation almost parallel, e.g., vec("China") − vec("Beijing") ≈ vec("Japan") − vec("Tokyo").

the embeddings are usually learnt by minimizing a global loss function of all the entities and relations in the knowledge graph.


We use stochastic gradient descent (SGD) to maximize the simplified objectives.

we can state the triplet of (w, r wv , v) is a fact.


For (Mintz et al., 2009), we use the implementation in (Surdeanu et al., 2012) 4 .

it cannot be directly applied to complete knowledge graphs.


Also, related to the control, 7 users expressed their opinion about the auto mode and they liked it, similarly, 6 shoppers commented about the setting and they thought that it was satisfactory."

the first work (Ganesan et al., 2010) proposes a graph-based method for generating ultra concise opinion summaries that are more suitable for viewing on devices with small screens.


However, unlike , our method assumes no domain knowledge about the entity in terms of a user-defined feature taxonomy.

the P/S values for the three aspects are [+2], [+3] and [+2] respectively which indicate positive opinion on all aspects.


For example, the aspect "g3", "canon g3" and "canon" were manually collapsed as into "camera".

choosing between user study 1 and 2 is not a straightforward decision.


Through formalizing sentiment distribution consistency as soft constraint, we propose a novel unsupervised model in the framework of Posterior Regularization (PR) to cluster aspectrelated phrases.

inspired by the intuition, we conduct interval parameter estimation for parameter p f j (sentiment distribution for phrase f j ) with limited observations, and thus get a confidence interval for p f j .


If p a i (sentiment distribution for aspect a i ) is not in the confidence interval of p f j , we then are confident that they follow different distributions.

• SDC-MNB is remarkably better than baselines, particularly for the cellphone domain.


The example in Figure  1 also indicates this phenomenon.

we add as the weight of the bigram in order to include more important bigrams.


A few studies focus on multi-document scientific article summarization.

we design our system to generate related work sections according to the related work section structure mentioned above.


In (San Segundo et al., 2001) an M-best can-didate HMM recognizer for 10,000 Spanish city names is built for speech documents.

the number in Figure  6 refers to the precision and recall for the name misspellings in the CRIPCO corpus created via random scrambling.


The problem related to personal proper names was discussed in (Allan and Raghavan, 2002).

one corpus is part of the English Gigawords, LDC2009T13 (Parker et al., 2009) and the second corpus is Adige500k in Italian (Magnini et al., 2006).


The CDC system computes the probability of coreference for two mentions t and t' using a similarity metrics into a vectorial space, where vectors are made out of contextual features occurring with t and t' respectively (Grishman, 1994).

figure 2 shows that there are two types of corpora.


Nevertheless, noisy WA makes both analyzing WS and improving SMT quality quite hard.

(Chang et al., 2008) noticed that the words in CTB are often too long for SMT.


First, unsupervised WA is performed on the SMT training corpus where the Chinese sentences are treated as sequences of characters; then, the Chinese sentences are segmented by CTB segmenter and a dictionary of segmented words are built; finally, for each word in the dictionary, the relative frequency of being split at a certain position is cal-  Table 4: Extended features used in the CRF model for word splitting.

the word segmentation (WS) schemes of these annotated corpora are handcrafted for general usage, and may not be suitable for SMT.


For source-pivot parallel corpora, we randomly select 1M, 2M, 3M, 4M and 5M Chinese-English sentence pairs.

this work is supported by National Natural Science Foundation of China (61100093), and the State Key Development Program for Basic Research of China (973 Program, 2014CB340505).


However, the pivot model has characteristics characters.

on one hand, the time cost is doubled; on the other hand, the translation error of the source-pivot translation system will be transferred to the pivot-target translation.


10 As shown in Figure 4, accuracies for both stem and suffix prediction vary noticeably with the segmentation used.

for each phrase pair matching the input, the phrase BNN score P BNN-p is computed as follows: where a is the word-level alignment of the phrase pair (s,t) and {a i } is the set of target positions aligned to s i .


We assume the availability of a word segmentation function g that takes a target word t as input and returns its stem and suffix: g(t) = (σ, µ).

also when it comes to morphology, generation seems to be of secondary importance compared to better selection in our experimental setup.


Furthermore, we compared the n-gram models against a context-based approach under different corpus-specific parameters: (a) corpus comparability, which is relevant to the seed dictionary, and (b) the occurrence frequency of the terms to be translated.

the first r features are extracted from the source term, while the last r features from the target term.


It is also shown that the combination of compositional and contextual methods performs better than each of the clues, separately.

the model converges using only a few hundred instances.


denotes an incomplete (partial) character.

in this paper, we propose a novel neural network architecture that learns to encode a variable-length sequence into a fixed-length vector representation and to decode a given fixed-length vector representation back into a variable-length sequence.


We just report the BLEU result of grammars sampled using PYP as for all our schedules, since PYP always performs better than DP.

we sample the cut value z i according to Equation 8.


In addition to exchangeability, another important property of type-based MCMC is that the type of each site to be sampled should be independent of the assignment of the other sites sampled at the same time.

we also run an inexact parallel approximation of type-based MCMC in comparison with the non-parallel sampling to find out if parallel programming is feasible to speed up typebased MCMC sampling without affecting the performance greatly.


In SMT, maximum entropybased reordering model is often introduced as a better alternative to the commonly used lexicalized one.

in this situation, using dual MLR is very beneficial.


At the same time, we can hardly make use of domain-level and document-level information collected in TE and KE to promote the performance of WS.

in our model, the word type of the previous word w i−1 corresponds to a restaurant and the current word w i corresponds to a customer.


(punctuation marks) and X (a catch-all for other categories).

the universal reduced POS tagset proposed by Petrov et al.


A multiword expression (MWE) is a combination of words with lexical, syntactic or semantic idiosyncrasy (Sag et al., 2002;Baldwin and Kim, 2009).

for both datasets, the majority of incorrectly-labelled items were compositional but predicted to be non-compositional by our system, as can be seen in the relatively low precision scores in Tables 2 and 3.


All Definitions (ALLDEFS): In the case that there are multiple definitions for the MWE, calculate the lexical overlap for each independently and take a majority vote; in the case of a tie, label the component as non-compositional.

if a given component can be found in the definition, then it is inferred that the MWE carries the meaning of that component.


For disgust, the scores are much more peaked near zero, favouring the more narrow coverage of the SVM.

anger and disgust also correlate with them, although to a lesser extent, and could be considered as belonging to the same cluster.


linear penalties for the GP and SVM respectively.

setting a larger rank allows more flexibility in modelling task correlations.


In our experiments, individual sentences are presented in a single line on a screen, thus we only model a horizontal gaze position s t ∈ R. At test time, we observe novel eye movement patternsS = {S 1 , .

we model the distribution over the saccade amplitude given the saccadic type (Equation 4) as truncated Gamma distributions, given by 1 is the Gamma distribution with shape parameter α and scale parameter β, and Γ is the Gamma function.


Hence f l is: where C C C is the code vectors set of all training instances.

3 Experiments Compared with the single-label classification, the multi-label setting introduces the additional degrees of freedom, so that various multi-label evaluation metrics are requisite.


A gradient step is then made to optimize the pairwise hinge loss: We use m = 0.1 in our experiments.

note that models like logistic regression and SVM do not scale well if you have millions of hashtags, which we could handle in our models.


This is equivalent to using a structured discriminative model with rich features on ngrams of candidate compressions.

we limit the maximum iteration number to 50 and return the best primal solution y (i) among all previous iterations for cases that do not converge in reasonable time.


The above formulation of the word similarity model can be interpreted as a mixture model in which w is similar to w if any of the context probabilities agrees.

we demonstrate its application in word alignment and translation (Section 4) and then briefly discuss the novelty of our work in comparison to other methods (Section 5).


While the factor p(e | e) can be directly computed by the word similarity model, the factor p(f | f ) can be problematic because it vanishes for f out of vocabulary.

to guard against false positives, we can alternatively reformulate it as a product of experts (Hinton, 1999), where Z(w) is a normalization constant.


For example, Roth and Yih (2007) conducted joint inference over entity mentions and relations; Our previous work jointly extracted event triggers and arguments (Li et al., 2013), and entity mentions and relations (Li and Ji, 2014).

joint relation-event features also provided 0.6% F 1 gain for relation extraction.


The upper-bound time complexity of the decoding algorithm is is the maximum size of nodes, b is the beam size, and m is the sentence length.

a single model that can extract all of them has never been studied so far.


learning rate, size of the hidden layer, size of the word vectors) have been chosen by finetuning our event extraction system on the TST1+TST2 data set.

in our case, the most frequent words are not always the most relevant for the task of event role detection.


We can also note that the performance of our model is good even with a small amount of training data, which makes it a good candidate to easily develop an event extraction system on a new domain.

our proposal offers a promising path towards easier and faster domain adaptation.


This figure shows that Step 2 achieves higher precision than Step 1 at all levels of recall, indicating that the word sense tagging method in step 2 is useful for Step 1 Step 2 Step  a significant boost of recall, together with a little improvement in precision.

table 2 illustrates some high-frequency LVCs extracted by the method automatically.


Compared to previous work, our method can perform well with less training data.

we compare our method with the method in the previous work DPLVM+GI in Sun et al.


To be fair we use the same feature set in Sun et al.

(2009), which outperforms Tsuruoka et al.


The rest of the paper is organized as follows.

both lines end at a rather high error rate (around 30%, i.e.


However, lower thresholds tend to result in estimations with smaller standard derivations due to those precisions are estimated over much more triples than those with higher thresholds, which means the randomness will be smaller.

in previous RE tasks, Precision-Recall curves are mostly used to evaluate the systems' performances.


Since the precision of the triples with high confidences is crucial for the task of KBP, we still suggest choosing the explicit ones when there is a well-defined schema available in the KB, although implicit type expectations can result in higher overall precision.

our joint frameworks provide more evidences that can be used to well capture the reliability of an extraction.


In order to make our framework more flexible, we need to come up with an approach to implicitly capture the relations' type expectations, which will also be represented as preference scores.

the task often suffers from errors propagating from upstream entity linkers to downstream relation extractors.


Information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features.

we have shown that information extraction in certain genres and domains spans different mediatextual and visual.


We carried out experiments to answer the following questions: Is treating temporal scoping as state change detection in Contextual Temporal Profiles(CTPs) effective?

it is therefore not applicable to any other corpora but Wikipedia.


Our method is based on two related insights: i) the context of the entity at time t reflects the events happening to the entity and the state of the entity at time t. ii) the difference in context before, at time t − 1, and after, at time t, reflect the associated state change at time t. However an entity can undergo a multiplicity of changes at the same time.

in a country election event, the country's state changes and it obtains a new value for its hasPresident relation.


In future we can explore cross validation and a separate development set to define a weighted sum for combining these two similarities.

such methods do not scale due to the amount of constraints that must be hand-specified.


scores are computed from the extract factors y i : relation label being i m : number of mentions (sentences) for the given entity pair R: total number of relation labels (excluding the nil label) The following is the ILP formulation for the exact inference arg max P r(y, z|x i ) in the model based on the deterministic-or: i∈{R,nil} The first constraint restricts a mention to have only one label.


This is the same formulation as Hoffmann but expressed as an ILP problem.

this is done to create weakly labeled training data to train a classifier for relation extraction.